Machine learning in reservoir rocks characterization: Integrating seismic data resolution enhancement for seismic facies classification

Master of ScienceDepartment of GeologyAbdelmoneam RaefAmid increasing interest in the dual enhanced oil recovery (EOR) and carbon geological sequestration (CGS) programs, improved static reservoir models emerge as a requirement for well-guided decision-making pertaining to the design of injector-producer well-drilling patterns. To this end, this study utilizes unsupervised machine learning approach leveraged with seismic resolution data preconditioning and spectral analysis to evaluate seismic facies based on machine learning models of clustering in multi-attributes space of the Mississippian carbonates of Kansas. The study provides a benchmark for understanding seismic facies distribution and implications for reservoir aspects pertaining to Enhanced Oil Recovery (EOR) and/or Carbon Geological Sequestration (CGS) programs, especially when encountering sparse well-logs control. A 3D seismic reflection P-wave data and a suite of well-logs and drilling reports constitute the data used for seismic facies based on seismic attributes input to machine learning hierarchical analysis and K-means clustering models. The results of seismic facies, six facies clusters, are analyzed in integration with the target-interval estimated mineralogy (Calcite-Dolomite-Quartz) and a predicted reservoir porosity. The study unravels the nature of the seismic (litho)facies interplay with porosity, sheds light on interpreting unsupervised machine learning classification of Kansas Mississippian carbonates at multi-resolution levels, and paves the way for an improved static model to enable effective CO2-EOR and geosequestration decision making

CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification

Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning approaches necessitate massive labeled data. Labeling is laborious and time-consuming, particularly for 3D seismic data volumes. To overcome this challenge, we propose a semi-supervised method based on pixel-level contrastive learning, termed CONSS, which can efficiently identify seismic facies using only 1% of the original annotations. Furthermore, the absence of a unified data division and standardized metrics hinders the fair comparison of various facies classification approaches. To this end, we develop an objective benchmark for the evaluation of semi-supervised methods, including self-training, consistency regularization, and the proposed CONSS. Our benchmark is publicly available to enable researchers to objectively compare different approaches. Experimental results demonstrate that our approach achieves state-of-the-art performance on the F3 survey

THE EFFECT OF SUPERVISED FEATURE EXTRACTION TECHNIQUES ON THE FACIES CLASSIFICATION USING MACHINE LEARNING

The widely accepted supervised machine learning classification algorithms are used for the semi-automating of the feature extraction process. In the machine learning facies classification process, each wireline log is a feature in the feature space. Since features are important in classification decisions, using suitable features improves the performance of a classification algorithm. In this study, three feature sets are compared containing the original conventional features (well-logs), and the extracted features from the unsupervised PCA and supervised FDA methods, using two classifier algorithms, namely SVM and RF. The FDA showed an improvement in the performance of facies classifiers while PCA can even deteriorate the results. An F1 score of 0.61 averaged over the available 20 folds for the combination of FDA feature extractor and RF classifier is achieved. This represents a 5% improvement in the prediction accuracy, compared to the conventional use of wells information as features with an F1 score of 0.56. Moreover, the conventional method uses all seven well-logs while with the FDA we only use three features